Parent and Child-Reported Strengths of Children With ADHD
Bibliographic record
Abstract
A strength-based approach to childhood attention-deficit/hyperactivity disorder (ADHD) research highlights children’s positive attributes that can support their areas of difficulty. However, research on perceptions of a child’s positive attributes is understudied. Specifically, there is little research that examines strength-based perceptions of children with ADHD, and only one known article addresses parent perceptions of their children with ADHD. As such, this study analyzed parent and child-reported strengths in children with ADHD. Parent and child-reported strengths were measured using the Behavioral and Emotional Rating Scale—Second edition, Parent Form and Child Form (BERS-2). Results indicated that parents and children perceived strengths in the interpersonal, intrapersonal, and affective domains to be similar, falling in the Average range. However, children indicated their family involvement and school functioning fell within the Average range, whereas parents rated these domains below average. Positive parental perspectives of their children may promote positive parent-child interactions and serve as an overall protective factor for children with ADHD. Domains which parents and children see as strengths should be utilized to support areas of weakness. Strength-based research for children with ADHD and positive interventions utilizing strengths may benefit families with ADHD, as well as classroom teachers and school psychologists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".